Johns Hopkins University to create a Human Proteome Chip using Arrayit's technology

Arrayit Corporation (OTCBB: ARYC) announced today that the new High Throughput Biology Center at Johns Hopkins University School of Medicine in Baltimore Maryland purchased Arrayit's proprietary technology to create a Human Proteome Chip to discover biomarkers for autoimmune diseases including rheumatoid arthritis, inflammatory bowel disease, autoimmune hepatitis (first identified by Dr. Zhu), lupus, and others.

Arrayit's patented NanoPrint microarray platform deposits nanoliter quantities of proteins onto glass substrates to create the chips. Johns Hopkins, which strongly endorses the Arrayit Platform and NanoPrint robot, is also considering Arrayit's consumable stream of substrates and reagents.

"Because of the high capacity of the NanoPrint, we use it to print 17,000 human proteins on a single glass slide. You can see all 17,000 proteins for their auto-immunity, covering 80% of the human proteome," said Dr. Heng Zhu, Assistant Professor at Johns Hopkins. "We call that The Human Proteome Chip."

Arrayit NanoPrint platforms have been installed at other major research and diagnostics centers including Harvard University in Massachusetts, Sandia National Laboratories in New Mexico, Scripps Research Institute in Florida, The Biodesign Institute in Arizona, and Mount Sinai School of Medicine in New York City.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
Post a new comment
Post

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.

You might also like...
Wearables and machine learning predict five-year fall risk in Parkinson’s patients